DocumentCode :
592321
Title :
An improved Predictive Optimal Controller with elastic search space for steam temperature control of large-scale supercritical power unit
Author :
Liangyu Ma ; Lee, Khuan Y. ; Yinping Ge
Author_Institution :
Autom. Dept., North China Electr. Power Univ., Baoding, China
fYear :
2012
fDate :
10-13 Dec. 2012
Firstpage :
7024
Lastpage :
7029
Abstract :
Predictive optimal control (POC) combined with artificial neural networks (ANNs) modeling and advanced heuristic optimization is a powerful technique for intelligent control. But actual implementation of the POC in complex industrial processes is limited by its known drawbacks, including the oscillation resulting from random search direction, difficulty in meeting the real-time requirement, and unresolved adaptability and generalization ability of the ANN predictive model. In resolving these problems, an improved Intelligent Predictive Optimal Controller (IPOC) with elastic search space is proposed in this paper. A new simpler and high-efficiency Particle Swarm Optimization (PSO) algorithm is adopted to find the optimal solution in fewer epochs to meet the real-time control requirements. The system output error in each control step is fed back to adjust the search space dynamically to prevent control oscillation and also make it easier to find the optimal solution. An improved recurrent neural network with external delayed inputs and outputs is constructed to model the dynamic response of the highly nonlinear system. The proposed IPOC is used to superheater steam temperature control of a 600MW supercritical power unit. Extensive control simulation tests are made to verify the validity of the new control scheme in a full-scope simulator.
Keywords :
dynamic response; generalisation (artificial intelligence); intelligent control; nonlinear systems; optimal control; particle swarm optimisation; predictive control; recurrent neural nets; temperature control; thermal power stations; ANN modeling; ANN predictive model; IPOC; PSO algorithm; advanced heuristic optimization; artificial neural networks; control oscillation; dynamic response; elastic search space; extensive control simulation tests; full-scope simulator; generalization ability; highly nonlinear system; intelligent control; intelligent predictive optimal controller; large-scale supercritical power unit; optimal solution; particle swarm optimization; random search direction; real-time control requirements; real-time requirement; recurrent neural network; superheater steam temperature control; system output error; unresolved adaptability; Aerospace electronics; Artificial neural networks; Boilers; Loading; Real-time systems; Valves;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control (CDC), 2012 IEEE 51st Annual Conference on
Conference_Location :
Maui, HI
ISSN :
0743-1546
Print_ISBN :
978-1-4673-2065-8
Electronic_ISBN :
0743-1546
Type :
conf
DOI :
10.1109/CDC.2012.6426325
Filename :
6426325
Link To Document :
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